Neural Argumentation Mining on Essays and Microtexts with Contextualized Word Embeddings

EasyChair Preprint 8738

Abstract

Detecting the argument components Claim and Premise is a central task in argumentation mining. Working with two annotated corpora from the genre of short argumentative texts, we extend a BiLSTM-CRF neural tagger to identify argumentative units and to classify their type (claim vs. premise). For the corpora we use, Persuasive Essays and Argumentative Microtexts, current methods relied on pre-computed non-contextual word embeddings such as Glove. In this paper, we adopt contextual word embeddings (Bert, RoBerta) and cast the problem as a sequence labeling task. We show that this step improves the state of the art for the Persuasive Essays, and we present strong initial results on applying the same approach to the Argumentative Microtexts.

Keyphrases: Contextualized Word Embeddings, Natural Language Processing, argumentation mining

@booklet{EasyChair:8738,
  author    = {Mohammad Yeghaneh Abkenar and Manfred Stede and Stephan Oepen},
  title     = {Neural Argumentation Mining on Essays and Microtexts with Contextualized Word Embeddings},
  howpublished = {EasyChair Preprint 8738},
  year      = {EasyChair, 2022}}